scholarly journals Type I Error Rates of the Kenward-Roger F-test for a Split-Plot Design with Missing Values and Non-Normal Data

2008 ◽  
Vol 7 (2) ◽  
pp. 385-397 ◽  
Author(s):  
Miguel A. Padilla ◽  
YoungKyoung Min ◽  
Guili Zhang
Keyword(s):  
Missing Values ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Split Plot ◽  
Plot Design

Covariance Structure Selection and Type I Error Rates in Split-Plot Designs

Methodology ◽  
2013 ◽  
Vol 9 (4) ◽  
pp. 129-136 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Type I Error ◽  
Covariance Structure ◽  
Error Rates ◽  
Information Criteria ◽  
Covariance Structures ◽  
Future Research ◽  
Dispersion Matrix ◽  
Type I ◽  
Type I Error Rates ◽  
Split Plot

We examined the selection of covariance structures and the Type I error rates of the Criterion Selector Akaike’s (Akaike’s Information Criteria, AIC) and the Correctly Identified Model (CIM). Data were analyzed with a split-plot design through the Monte Carlo simulation method and SAS 9.1 statistical software. We manipulated the following variables: sample size, relation between group size and dispersion matrix size, type of dispersion matrix, and form of the distribution. Our findings suggest that AIC selects heterogeneous covariance structure more frequently than original covariance structure. Specifically, AIC mostly selected heterogeneous covariance structures and displayed slightly higher Type I error rates than the CIM. These were mostly associated with main and interaction effects for the ARH and RC structures and a marked tendency toward liberality. Future research needs to assess the power levels exhibited by covariance structure selectors.

Type I Error Rates for Parscale’s Fit Index

2005 ◽  
Vol 65 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Christine E. Demars
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Fit Index

scholarly journals Control of Type I Error Rates in Bayesian Sequential Designs

2019 ◽  
Vol 14 (2) ◽  
pp. 399-425 ◽  
Author(s):  
Haolun Shi ◽  
Guosheng Yin
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Sequential Designs ◽  
Type I Error Rates

scholarly journals Sisvar: a Guide for its Bootstrap procedures in multiple comparisons

2014 ◽  
Vol 38 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Daniel Furtado Ferreira
Keyword(s):  
Scientific Community ◽  
Type I Error ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Statistical Analysis System ◽  
Scientific Results ◽  
Analysis System ◽  
Multiple Comparison Procedures

Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.

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